The goal of hydrocarbon exploration is to find porous and permeable geologic deposits containing high pore-space saturations of hydrocarbons, under sufficient pressure to allow some mode of commercial production. In pursuit of this goal, companies, countries and individuals collect and process many types of geophysical and geological data. The data are often analyzed to find anomalous zones that can reasonably be attributed to the presence of hydrocarbons.
The usage of 2D and 3D seismic data anomalies has been a standard practice in the petroleum industry since the 1960s. Other geologic and geophysical data anomalies have been tried, sometimes successfully, for over a century. These include various gravimetric, electromagnetic, chemical, biological and speculative methods.
The usage of anomalies for oil and gas detection has been plagued by several problems. First, most remote sensing anomalies (e.g., a 3D seismic amplitude anomaly) cannot be directly tied to a rock property that could be measured in the laboratory or using well logs. Much effort is expended attempting to tie observed anomalies to known rock responses by modeling the expected attribute response or otherwise correlating with a known producing reservoir. This work is often based on the experience of the practitioner.
A second problem is that the anomalies themselves are often evaluated or tied to response models in a qualitative manner. With qualitative assessment as the basis, quantitative, objective and reproducible error analysis has not been possible.
A third problem is that a basic physical property at work in hydrocarbon reservoirs is that both oil and gas are less dense than water. This generally causes oil and gas to accumulate up-structure in the pore-space of potential reservoir rocks. The higher water saturations are found, generally, down-structure. This separation of saturations is driven by gravity. When such a separation of fluid types occurs, flat interfaces, in depth, are expected to form.
This separation causes numerous possible classes of data attribute response. First, the hydrocarbon reservoir will have one response for each hydrocarbon type. The water-saturated part of the reservoir may have a second data response and the interfacial area a third type of attribute data response. This sequence of responses in the processed attribute data allows for a simultaneous analysis of the three classes.
Another problem is that the strengths of many types of data attribute anomalies are dependent on the rock physics of the geologic systems. Some anomalies are very evident in the data. Others can be very subtle and cause considerable debate. An associated problem is that much work in hydrocarbon exploration continues to be done in areas where the data are poor, noisy or difficult to interpret. In areas of good data quality, many high-strength anomalies are adequately interpreted by inspection. As the data quality and/or imagining ability of the data degrade, verifying whether a legitimate anomaly does or does not exist in a given set of data is very difficult, especially when the rock physics suggests that any meaningful anomaly would be subtle.
The lack of quantification, error analysis, subjectivity of analysis and data quality issues cause variations in the appraisal of data anomalies in oil and gas exploration and production projects. It is not uncommon for different individuals or companies to examine the same anomaly and reach irreconcilably, different conclusions. In many cases, explaining quantitatively why the anomaly of one prospect should be “believed or trusted” more than that of another prospect has not been possible. This lack of trust causes different entities to make drastically different investment decisions concerning prospects based on the same underlying data.
The present embodiments are designed for the quantification and evaluation of data anomalies in the search for producible hydrocarbon deposits. The present embodiments are designed to simultaneously quantify and summarize the hydrocarbon reservoir part of the anomaly, the water reservoir part of the data and the interfacial zone. The embodiments address the case of multiple hydrocarbon zones, e.g., gas over oil over water. The embodiments are designed to test the model wherein gas is less dense than oil and oil is less dense than water, with data responses varying by structural position.
The current embodiments can be used for the quantification of changes in lithology, facies, or rock fabric from one location to another. The current embodiments are designed to function in areas of low signal-to-noise and aid in the determination of data suitability for hydrocarbon detection for the expected rock physics environment. The current embodiment, therefore, can be applied to the detection of subtle hydrocarbon related data anomalies.
The prior art includes isolated instances of attempts to include background analysis of seismic traces to find zones which are anomalous with respect to that background. U.S. Pat. No. 5,001,677 teaches an approach wherein multiple seismic attributes are assigned to a vector space and a background vector is constructed using, in the preferred implementation, the median of a set of attribute vectors along a seismic trace, below a given location on the earth. A distance is measured from the background vector to the data vector at a location of interest. U.S. Pat. No. 6,058,074 teaches an approach to amplitude versus offset trace processing wherein the traces are scaled by means and standard deviations of the data in background windows. Two new volumes of appropriately scaled trace intercept values and trace gradient values are then produced. U.S. Pat. No. 5,862,100 teaches the extraction of anomalous AVO points from associated background points using a statistical description of the AVO background data. This robust method is used when the background can be described by a single statistical distribution. The taught method lacks the ability to handle structured non-statistical background clusters and suffers from sampling errors on some datasets if all background windows are chosen identically.
A need exists for a method to scan large amounts of geophysical data sets systematically and simultaneously to find the presence of hydrocarbons. The method should honor non-statistical and highly structured (due to geology and rock properties) host rock geophysical responses. The method should honor small changes in the host rock layering or composition in constructing background data volumes for normalization and scanning.
The present embodiments meet these needs.
The present method is detailed below with reference to the listed Figures.